Bayesian Variational Inference for Exponential Random Graph Models
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Publication:5066760
DOI10.1080/10618600.2020.1740714OpenAlexW3011604461WikidataQ115550153 ScholiaQ115550153MaRDI QIDQ5066760
Publication date: 30 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1811.04249
stochastic variational inferencenonconjugate variational message passingexponential random graph modelimportance weighted lower boundadaptive self-normalized importance samplingadjusted pseudolikelihood
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Cites Work
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- Variational inference for generalized linear mixed models using partially noncentered parametrizations
- Bayesian computation for statistical models with intractable normalizing constants
- Gaussian variational approximation with sparse precision matrices
- Asymptotic bias of stochastic gradient search
- On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
- Simple statistical gradient-following algorithms for connectionist reinforcement learning
- A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
- Bayesian Model Selection for Exponential Random Graph Models via Adjusted Pseudolikelihoods
- Goodness of Fit of Social Network Models
- Sequential Imputations and Bayesian Missing Data Problems
- Introduction to Stochastic Search and Optimization
- Bayesian Inference in the Presence of Intractable Normalizing Functions
- Marginal Likelihood From the Metropolis–Hastings Output
- Bayes Factors
- An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
- A Stochastic Approximation Method
